Wastewater Treatment Pond Algal Production for Biofuel

Chapter
Part of the Cellular Origin, Life in Extreme Habitats and Astrobiology book series (COLE, volume 25)

Abstract

Wastewater treatment high rate algal ponds fertilised with CO2 (HRAP + C system) provide a niche opportunity for algal biofuel production in combination with energy-efficient and cost-effective tertiary-level wastewater treatment. Wastewaters are excellent media (water, nutrients, alkalinity buffering) for the growth of naturally occurring algae that could be harvested by bioflocculation and low-cost gravity settling, especially when fertilised with CO2 obtained from biogas produced and used for power generation at the treatment plant. The harvested algal biomass is converted to biofuels, preferably by processes that use the entire biomass with little or no dewatering. Anaerobic digestion of the algal biomass along with settled primary sludge is the most readily available and economic technology. The wastewater treatment function can essentially cover the entire capital and operation costs of biofuel production. Additional environmental and financial incentives are due to GHG abatement and nutrient fertiliser recovery. Since wastewater treatment systems using facultative ponds are already a widely used technology for secondary-level wastewater treatment, upgrading these ponds provides an opportunity to develop and refine algal production and harvest from HRAP systems and algal biofuel conversion technologies. The HRAP + C system can produce biogas for power generation at essentially no additional cost to that incurred for wastewater treatment alone. Additional research, in particular, on selection and cultivation of superior algal strains, grazer control and cost-effective algal harvest is still required before widespread adoption of this technology is possible.

Keywords

Wastewater Treatment Anaerobic Digestion Algal Biomass Nutrient Removal Algal Biofuel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

 Acknowledgements

The authors wish to thank Jason Park, Stephan Heubeck and Ian Woertz who provided valuable contributions to this chapter. NIWA funding was provided by the New Zealand Foundation for Research Science and Technology.

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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Rupert J. Craggs
    • 1
  • Tryg Lundquist
    • 2
  • John Benemann
    • 3
  1. 1.National Institute of Water and Atmospheric ResearchHamiltonNew Zealand
  2. 2.Department of Civil and Environmental EngineeringCalifornia Polytechnic State UniversitySan Luis ObispoUSA
  3. 3.MicroBio Engineering, Inc.Walnut CreekUSA

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